Open AccessSCRIB: Set-Classifier with Class-Specific Risk Bounds for Blackbox Models
Author(s) -
Zhen Lin,
Cao Xiao,
Lucas M. Glass,
M. Brandon Westover,
Jimeng Sun
Publication year - 2022
Publication title -
proceedings of the ... aaai conference on artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2374-3468
pISSN - 2159-5399
DOI - 10.1609/aaai.v36i7.20714
Subject(s) - classifier (uml) , computer science , artificial intelligence , machine learning , class (philosophy) , pattern recognition (psychology) , data mining
Despite deep learning (DL) success in classification problems, DL classifiers do not provide a sound mechanism to decide when to refrain from predicting. Recent works tried to control the overall prediction risk with classification with rejection options . However, existing works overlook the different significance of different classes. We introduce Set-classifier with Class-specific RIsk Bounds (SCRIB) to tackle this problem, assigning multiple labels to each example. Given the output of a black-box model on the validation set, SCRIB constructs a set-classifier that controls the class-specific prediction risks. The key idea is to reject when the set classifier returns more than one label. We validated SCRIB on several medical applications, including sleep staging on electroencephalogram (EEG) data, X-ray COVID image classification, and atrial fibrillation detection based on electrocardiogram (ECG) data. SCRIB obtained desirable class-specific risks, which are 35%-88% closer to the target risks than baseline methods.